Cardiac Beat Research Articles

The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.

Early prediction of sudden cardiac death risk with Nested LSTM based on electrocardiogram sequential features

Electrocardiogram (ECG) signals are very important for heart disease diagnosis. In this paper, a novel early prediction method based on Nested Long Short-Term Memory (Nested LSTM) is developed for sudden cardiac death risk detection. First, wavelet denoising and normalization techniques are utilized for reliable reconstruction of ECG signals from extreme noise conditions. Then, a nested LSTM structure is adopted, which can guide the memory forgetting and memory selection of ECG signals, so as to improve the data processing ability and prediction accuracy of ECG signals. To demonstrate the effectiveness of the proposed method, four different models with different signal prediction techniques are used for comparison. The extensive experimental results show that this method can realize an accurate prediction of the cardiac beat’s starting point and track the trend of ECG signals effectively. This study holds significant value for timely intervention for patients at risk of sudden cardiac death.

Early diagnosis of caesarean scar pregnancy by ultrasound - a novel, simple and rapid clinical classification scoring system.

In the recent years, with the increase in the caesarean section rate, the incidence of caesarean scar pregnancy (CSP) has shown a significant upwards trend. We propose a novel, simple and rapid clinical and ultrasound (US) classification scoring system to assist in the early diagnosis of CSP. A total of 385 patients with CSP were included in the study. All patients were given a comprehensive score, iincluding clinical data (whether HCG is consistent with gestational age and vaginal bleeding) and US findings (linea a and b, gestational sac morphology, the presence of primitive cardiac tube beat, and color Doppler aspect). The scores were analysed by ROC curve analysis, and sensitivity and specificity were calculated. A score of 4 has a specificity of 91.7% and a sensitivity of 95.6% in diagnose CSP. The area under the ROC curve was 0.973. This scoring system may be a reliable tool for the early diagnosis of CSP and has the characteristics of being simple and rapid. For patients with a total score of ≥4 points, CSP is suggested, and early clinical treatment can be carried out, while patients with a score of less than 4 points can temporarily retain pregnancy and be closely followed up.

First presentation of atrial fibrillation with pre-excitation syndrome in octogenarian

Palpitation is a symptom of cardiac arrhythmia commonly presented at hospital admission. It has a wide range of clinical importance, from subtle insignificant cardiac ectopic beats to life-threatening cardiac arrhythmias with a risk of sudden cardiac death. Approach to palpitation emphasizes on the identification of its primary cause which relies strongly on the underlying risk factors and age groups. Cardiac arrhythmias due to innate cardiac conductive disorders such as accessory pathway in Wolff-Parkinson-White syndrome tend to present at a younger age while arrhythmias related to ischemic heart disease occur mostly in the elderly population. We report a case of first diagnosed atrial fibrillation in an 81-year-old patient with previously unknown pre-excitation syndrome.

MODELING AND SYNTHESIS OF THE WISTAR RAT ELECTROCARDIOGRAM

In this work, we present a synthesizer of electrocardiographic signals of the experimental model of the Wistar rat. Firstly, we modeled the P, R, and S waves using Gaussian functions, while for the T-wave, we used a Gumbel function. We validated the proposed model using a Wistar rat electrocardiogram database, which contains 41 animals with 62,907 beats. Model global performance was CC (correlation [Formula: see text], and RE (relative [Formula: see text]. We also propose a transformation that allows the aforementioned model to be built from the standard ECG parameters, such as RR interval, P wave duration, PR interval, RS interval, QT interval, and P, R, S, and T wave amplitudes. In all cases, the correlation coefficients obtained were greater than 0.966, while the mean of the absolute errors of the parameters did not exceed 6[Formula: see text]ms and 30[Formula: see text][Formula: see text]V. Also, we have developed software that integrates the proposed model and the transformation. We used a system of coupled differential equations to synthesize several beats based on one cardiac beat pattern with the possibility of entering variability in the ECG parameters and adding noise to give realism to the electrocardiographic signals. In conclusion, we developed a synthesizer of Wistar rats ECG useful for several applications, such as delineation and segmentation, training and testing of automatic algorithms to detect and monitor, as well as novel filtering methods of these types of ECG signals.

Coffee drinking and cardiac ectopic beats: Black or White?

Coffee drinking and cardiac ectopic beats: Black or White?

An analysis of misoprostol effectiveness in second trimester pregnancy terminations

Background/Aim: Misoprostol is frequently used as a single agent in pregnancy terminations. However, it increases the risk of uterine rupture in patients who have had previous uterine surgery and terminations due to stillbirths. Therefore, it is used with concern by clinicians. The aim of this study was to evaluate the clinical features of the groups that responded and did not respond to termination treatment with misoprostol in a tertiary center and to investigate its efficacy and safety. Methods: The study design was comprised of a retrospective cohort study. A total of 114 second trimester pregnancies (between 13-24 weeks gestational age) were included in the study. These pregnancies were indicated for termination based on the prenatal diagnosis unit for fetal or maternal causes. According to the International Federation of Gynecology and Obstetrics (FIGO) directions, misoprostol was applied in the following dosages: for 13-17 weeks gestational ages, one tablet per 6 hours; for 18-26 gestational ages, ½ tablet per 6 hours; and for other indications 2 tablets per 3 hours were administered. If the patient had had a previous cesarean operation, all doses were halved. After the first 24 hours, the percentage and demographics results, such as age, body mass index (BMI), gravida, number of cesareans, number of curettages, cervical lengths, BISHOP scores, gestational age, amniotic fluid index, and fetal cardiac beat of the patients with miscarriage, were recorded. Results: The number of cases resulting in miscarriage within 24 hours were 84 (73.7%) and within 48 hours were 14 (12.2%). The total of misoprostol doses used were 8 tablets of 200 mg, mean time until the complete abortion was 17 hours. Sixteen patients required additional treatment, of whom four required Foley catheterization, five required D&E, seven required resting, and no one required a hysterectomy. Uterine rupture occurred in two patients who needed laparotomic surgery. The maternal age (P=0.340), BMI (P=0.790), gravida (P=0.270), previous cesarean history (P=0.390), previous curettage number (P=0.520), cervical length (P=0.380), Bishop score (P=0.190), gestational age (P=0.072), amniotic fluid index (P=0.470) and presence of fetal cardiac beat (P=0.350) were similar between groups Conclusion: Our results indicated that misoprostol is a safe, useful, and effective treatment option for second trimester medical terminations. Caution should be exercised in its use in patients with a history of uterine surgery.

Abstract 113: Detection Of Patent Foramen Ovale In Patients With Ischemic Stroke On Prospective ECG-gated Cardiac CT Compared To Transthoracic Echocardiography

Background: Cardiac CT acquired during the acute stroke imaging protocol is an emerging alternative to transthoracic echocardiography (TTE) to screen for sources of cardioembolism, but its diagnostic accuracy to detect patent foramen ovale (PFO) is unclear. Methods: This was a substudy of Mind the Heart, a prospective single-center cohort study in which consecutive adult patients with acute ischemic stroke underwent prospective ECG-gated cardiac CT during the initial stroke imaging protocol. Patients also underwent routine work-up, including TTE. We included patients <60 years who underwent TTE with agitated saline contrast (cTTE). Presence of a PFO on CT and cTTE was scored according to predefined criteria by a cardioradiologist and cardiologist, respectively, who were blinded to each other’s scoring. On CT, PFO was defined as a crypt shaped contrast jet from the left atrium to the right atrium towards the vena cava or an atrium septum discontinuity. On cTTE, a PFO was defined as the appearance of microbubbles in the left atrium within 3-6 cardiac beats after opacification of the right atrium. We assessed the sensitivity and specificity of cardiac CT for the detection of PFO using cTTE as the reference standard. Results: Of 452 patients in Mind the Heart, 92 were younger than 60 years. Of these, 56 (61%) patients underwent both cardiac CT and cTTE and were included. Median age was 55 (interquartile range [IQR] 49-57) years and 38 (68%) were male. In total, 32/56 (57%) patients had cryptogenic stroke and their median Risk of Paradoxical Embolism score was 6 (IQR 6-7). Cardiac CT detected a PFO in 5/56 (9%) patients, 3 of which were confirmed on cTTE. cTTE detected a PFO in 12/56 (21%) patients. The sensitivity and specificity of cardiac CT were 25% (95%CI: 5-57%) and 95% (95%CI: 85-99%), respectively. Positive and negative predictive value were 59% (95%CI: 22-87) and 83% (95%CI: 77-87), respectively Conclusion: Due to its low sensitivity, prospective ECG-gated cardiac CT does not appear to be a suitable screening method for PFO. If cardiac CT is used to screen for cardioembolism, additional echocardiography remains indicated in patients with cryptogenic stroke in whom PFO detection would have therapeutic consequences.

Maternal serum 25-hydroxy vitamin D level in first-trimester pregnancy loss

Aim: Pregnancy loss (abortus) is defined as loss of fetal cardiac beat or expulsion of pregnancy material before 20 weeks.It has many causes such as chromosomal abnormalities, uterine anomalies, infections, and unknown causes. Vitamin D has become one of the new topics of research in many medical fields, as it has relations with other diseases besides bone diseases. In some studies of recurrent pregnancy loss, a low vitamin D level was found. In our study, vitamin D levels were measured in the first-trimester pregnancy loss and normal pregnancy group. Methods: Patients, single pregnancies with a positive fetal heartbeat, who applied to the pregnant outpatient clinic for the first examination of pregnancy were included in the study, and vitamin D levels were measured at this time. The patients were followed up to the 12th gestational week using the hospital automation system. The groups of normal pregnancy and abortus groups were determined. 25-hydroxy (25-OH) vitamin D levels between 2-96 ng/ml can be detected, while over 30 ng/ml is sufficient, 20-29.99 ng/ml is insufficient, and <20 ng=''/ml is defined as a low 25(OH)D3 level. Results: 66 patients were included in the pregnancy loss group, in the follow-up, 63 patients were in the normal pregnancy group. The mean vitamin D level of the individuals included in the study was measured as 7.45�4.64 ng/ml. Levels of vitamin D were 10.04�6.19 in the normal course group and 4.8�1.73 ng/ml in the intrauterine exitus group (p<0.05). At='' a='' vitamin='' D='' level='' of='' 6,87='' ng=''/ml, pregnancy loss was expected with 90% sensitivity and 64% specificity. Conclusion: It is still an important issue to determine the most appropriate level and preconceptional starting and the appropriate dose for maximum benefit for mother and baby, especially in the reproductive period and pregnant. Therefore, large-scale randomized controlled studies of high quality are needed.

Heart Rate Variability and Cortisol Levels Before and After a Brief Anaerobic Exercise in Handball Players.

Kayacan, Y, Makaracı, Y, Ucar, C, Amonette, WE, and Yıldız, S. Heart rate variability and cortisol levels before and after a brief anaerobic exercise in handball players. J Strength Cond Res 37(7): 1479-1485, 2023-Evaluating stress in athletes is important for monitoring overall physiologic load and is a core practice for sport performance teams. This study examined relationships between 2 metrics of training stress, heart rate variability (HRV) and cortisol, before and after intense anaerobic power testing. Electrocardiogram recordings and saliva samples were collected before and immediately after a Wingate anaerobic power test (WAnT-30) from professional handball players ( n = 20) and sedentary controls ( n = 18). Between-group differences and correlations were computed to assess study hypotheses. No differences were observed in HRV frequency-dependent parameters between groups, but in athletes, Min. R-R ( p < 0.01) and Avg.R-R ( p = 0.03) before WAnT-30 and the percentage of successive normal cardiac beat intervals greater than 50 milliseconds (i.e., pNN50; p = 0.03) after WAnT-30 were elevated. A high positive correlation was detected between the pretest and post-test cortisol levels in athletes ( p = 0.0001; r = 0.87) but not in sedentary individuals. No correlations were observed between the cortisol levels and WAnT-30 power parameters in either group. Relationships were evident in the standard deviation of RR intervals ( p = 0.02, r = -0.53), square root of the mean squared difference of successive RR intervals ( p = 0.043, r = -0.46), very low frequency ( p = 0.032; r = -0.480), high-frequency ( p = 0.02; r = -0.52) variables, and pretesting cortisol in athletes. These findings suggest that HRV analysis is a valuable tool for examining cardiovascular regulation, independent of cortisol; the data may provide valuable information for performance teams in evaluating acute stress.

Relationship among surface electric double layer of cardiomyocyte membrane and toxicology of digoxin and opening of ion channels

We applied a new idea that the potential effect can change the ion adsorption structure on the cell surface to explore the mechanism of digoxin poisoning and the regulation of ion channels. The effects of digoxin on the electrophoretic mobility and behaviors (non-contraction or contraction or autorhythmicity) of cardiomyocytes were observed by single-cell electrophoresis technique (imitate the opening method of in vivo channel) and the method of decomposing surface potential components on the cells. As well as affect the association with electrical activity. The results suggested that the increase of cardiomyocytes transmembrane potential and the Na+–K+ exchange on the cell surface of the action potential phase 4 caused by the poisoning dose of digoxin, leading to the oscillation of adsorbed ions on the cell surface and the incomplete channel structure, which were the mechanism of cardiac ectopic beats. The results revealed that the opening of ion channels is regulated by the surface electric double layer of the cell membrane.

ECG diagnosis for arrhythmia detection with a cloud-based service and a wearable sensor network in a smart city environment

Irregular heartbeats are a primary indicator of Cardiovascular Disease (CVD), which is the leading cause of death in a developing smart city environment. Wearable devices can reliably monitor cardiac beats by producing Electrocardiogram (ECG) readings. The considerable value gained from a wireless wearable system allows for remote ECG assessment with continuous real-time functionality. The data collected from the wearable sensor network in the smart city platform gives timely alarms and treatment that could save lives. Cloud-based ECG methods can be accurate to a certain extent, as latency is still an existing problem. Cloud-based portals linked immediately to wearable devices can provide numerous advantages, such as reduced latency and a good level of service. Therefore, a novel cloud-based arrhythmia detection using the Recurrent Neural Network (RNN) (NC-RNN) method has been proposed for the ECG diagnosis with a wearable sensor in the smart city environment. The ECG signal collected from the wearable sensor involves three phase diagnosis stage. R-peak detection techniques are used for preliminary diagnostics in edge devices. The ECG signals are then classified using RNN at the edge device, with the severity of irregular beat detected in the ECG signal. Finally, a cloud platform classification method can evaluate the obtained ECG signals. While the proposed method's training session is runnable on the technically rich Cloud data centers, the interpretation unit is deployed over the cloud infrastructure for evaluating the ECG signals and setting off the emergency remedies with minimum latency. The simulation results of the suggested framework can accomplish effective ECG detection via wearable devices with high accuracy and less latency.

Performing the classification of pulsation cardiac beats automatically by using CNN with various dimensions of kernels

The clinical indication of arrhythmia identifies specific aberrant circumstances in heart pumping that may be detected using electrical impulses during conduction or by allowing a little amount of current to travel through the electrodes, disrupting the cardiac muscle's resistance. The electrocardiogram (ECG) is one of the most important instruments for detecting cardiac arrhythmia since it is the most least intrusive and effective procedure. Physically or visually inspecting the heart is time-consuming and difficult, hence the development of computer aided diagnosis (CAD) is being developed to aid clinical decision-making. In this suggested research, a convolutional neural network (CNN)-based approach is used to automate the heartbeat classification process in order to identify cardiac arrhythmia. The improved enhancement of CNN structure has been implemented in this suggested research. The feature maps are then subjected to the max pooling process. Finally, feature maps are generated by concatenating kernels of different sizes and delivering them as an input to the fully linked layers. The MIT BIH arrhythmia database is used to implement this approach, and the total average accuracy is 99.21%. The proof of the suggested study's efficiency and efficacy in identifying cardiac arrhythmia has also been done via an experimental comparison

A novel weighted approach for automated cardiac arrhythmia beat classification using convolutional neural networks

A novel weighted approach for automated cardiac arrhythmia beat classification using convolutional neural networks

Numerical simulation of transcatheter mitral valve replacement: The dynamic implication of LVOT obstruction in the valve-in-ring case

Transcatheter mitral valve replacement (TMVR) has been used for “off-label” treatment when annuloplasty band ring for mitral repair fails. However, the complex anatomy and function of the mitral valve may lead to fatal complications as a result of the left ventricular outflow tract (LVOT) obstruction in TMVR. We report the structural and hemodynamic response of LVOT obstruction resulting from TMVR with the Edwards SAPIEN 3 Ultra (S3) device. We modified the original Living Heart Human Model (LHHM) to account for a failed mitral valve with an annuloplasty band ring and simulated the cardiac beating condition in the setting of S3 device implantation. Findings demonstrated a high dynamic behavior of the newly formed LVOT (neoLVOT) as confined by the displaced mitral valve and the interventricular septum. During the cardiac beat, the neoLVOT area oscillated from a maximum of 472.1 mm2 at early systole to the minimum of 183 mm2 at end-systole. The profile of both anchoring force and contact pressure revealed that the band ring serves as the anchoring zone while mitral valve is primally displaced by the deployed device. At early systole, computational flow dynamics highlighted hemodynamic disturbances associated with the LVOT obstruction, with a skewed flow towards the septum and a pressured drop of 4.5 mmHg between the left ventricular apex and the neoLVOT region. This study can lead to a more accurate assessment of the risk induced by the LVOT obstruction when stratifying patient anatomic suitability for TMVR.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update.

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

Detection of cardiac arrhythmias from ECG signals using FBSE and Jaya optimized ensemble random subspace K-nearest neighbor algorithm

Cardiac Arrhythmias is defined as the sudden and unexpected occurrence of a rhythm in the heart. Arrhythmia detection is prevalent in developing countries that require systems-based screening solutions for diagnosis. A new rhythm-based approach is proposed to screen the patients with cardiac arrhythmia at the primary level. This method eliminates multiple tests, enabling faster and more accurate diagnosis. This method first segments the electrocardiogram signals to create a complete picture of the single heartbeat. Then the Fourier–Bessel series expansion (FBSE) is used to transform the sequences of each heartbeat into more meaningful ones that can characterize the structural integrity of arrhythmia. The FBSE sequence series are trained using the Jaya optimized ensemble random subspace KNN (JO-ERSKNN) model with 10-fold cross-validation for classifying five types of cardiac arrhythmia beats. We have achieved an accuracy of 99.49%, a sensitivity of 95.43%, and specificity of 99.48%. The results demonstrate that the proposed algorithm can detect differences in the five types of cardiac arrhythmia signals. It can also be utilized as a screening tool for detecting arrhythmia and can be made compatible with various wearable devices.

RETRACTED ARTICLE: Cardiac arrhythmia detection using dual-tree wavelet transform and convolutional neural network

The non-stationary ECG signals are used as key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells and depolarization and re-polarization conducted in a synchronized manner as: the P wave occurs first, followed by the QRS-complex and the T wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient’s heart status. Simple naked eye diagnosis can mislead the detection. At that point, computer-assisted diagnosis (CAD) is therefore required. In this paper dual-tree wavelet transform is used as a feature extraction technique along with deep learning (DL)-based convolution neural network (CNN) to detect abnormal heart. The findings of this research and associated studies are without any cumbersome artificial environments. This work investigates the viability of using deep learning-based architectures for heartbeat classification. The DL architecture is used for the proposed project, and the results suggest that it is feasible to use 2D images to train a deep learning architectures for heartbeat classification. The CNN produced the highest overall accuracy of around 99%. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

Pattern Recognition and Analysis: Neural Network using Weighted Cross Entropy

In order to identify arrhythmia more simply and efficiently, one-dimensional convolutional neural network is used to extract electrocardiogram signal features and a neural network model based on cardiac beat is established, which can identify eight major ECG arrhythmia types. The cross entropy loss function is weighted according with the data distribution to solve the data imbalance problem. Experiments verify the effectiveness of the weighted cross-entropy loss function in ECG recognition, which improves the accuracy of all kinds to more than 99.5% and the sensitivity of all kinds to more than 90%. Compared with other models, this model can improve the performance of ECG arrhythmia recognition.

Bioprosthetic Total Artificial Heart in Autoregulated Mode Is Biologically Hemocompatible: Insights for Multimers of von Willebrand Factor.

Carmat bioprosthetic total artificial heart (Aeson; A-TAH) is a pulsatile and autoregulated device. The aim of this study is to evaluate level of hemolysis potential acquired von Willebrand syndrome after A-TAH implantation. We examined the presence of hemolysis and acquired von Willebrand syndrome in adult patients receiving A-TAH support (n=10) during their whole clinical follow-up in comparison with control subjects and adult patients receiving Heartmate II or Heartmate III support. We also performed a fluid structure interaction model coupled with computational fluid dynamics simulation to evaluate the A-TAH resulting shear stress and its distribution in the blood volume. The cumulative duration of A-TAH support was 2087 days. A-TAH implantation did not affect plasma free hemoglobin over time, and there was no association between plasma free hemoglobin and cardiac output or beat rate. For VWF (von Willebrand factor) evaluation, A-TAH implantation did not modify multimers profile of VWF in contrast to Heartmate II and Heartmate III. Furthermore, fluid structure interaction coupled with computational fluid dynamics showed a gradually increase of blood damage according to increase of cardiac output (P<0.01), however, the blood volume fraction that endured significant shear stresses was always inferior to 0.03% of the volume for both ventricles in all regimens tested. An inverse association between cardiac output, beat rate, and high-molecular weight multimers ratio was found. We demonstrated that A-TAH does not cause hemolysis or AWVS. However, relationship between HMWM and cardiac output depending flow confirms relevance of VWF as a biological sensor of blood flow, even in normal range.